Recently, there has been an increased demand for engine systems with internal combustion engines to meet criteria such as improved fuel economy and reduced emissions, all the while maintaining optimal performance for the user, which led to the development of technologies such as fuel injection systems, turbocharging, and exhaust gas recirculation that made the engines much more environmentally-friendly without sacrificing satisfactory user experience. As a result, more emphasis is placed on the optimization of multiple criteria, which includes balancing fuel economy, emissions, and engine performance to achieve as much as possible in all criteria at the same time, by controlling variables within the engine system in a stochastic environment, a process generally referred to as engine tuning.
Specifically, it is desirable to control an air handling system of an internal combustion engine, particularly during transient events, to provide for a responsive air handling system capable of responding appropriately to transient operating conditions. As such, the internal combustion engine, which uses a turbocharger and an exhaust gas recirculation (EGR) system to control the air flow inside the cylinder, requires efficient engine tuning to fully utilize the available components and achieve optimal performance.
Prior art techniques of engine tuning include model-based air handling controllers which employ model predictive controllers (MPC). A block diagram of such MPC system is illustrated in FIG. 1, which is largely divided into a MPC block 1 and a process block 2. The system uses a process model 3 (which acts parallel to the process 2), a prediction block 4, a set-point calculations block 5, and a control calculations block 6 to predict the current process outputs. The residuals, which are the difference between the predicted model outputs and the actual process outputs, are fed back into the prediction block 4, along with the inputs from the control calculations block 6 which receives the predicted outputs from the prediction block 4 and set points (targets) from the set-point calculations block 5 to determine the actual inputs to enter into the process 2. Thus, the system uses residuals to correct its future predictions such that adjustments are made to more closely match the process model 3 with the process 2. As such, the accuracy of the initial model used in the process model 3 is very important, since the computations depend heavily on how accurate the model 3 is with respect to the performance of the actual process 2. Also, MPC typically incorporates linear functions to imitate the actual process that is dynamic in nature. Therefore, attempting to approximate dynamic models as linear functions may not capture the actual behavior of the process, thereby leading to poor performance by the system. Furthermore, linear models may be able to predict the current outputs within a finite time-horizon, but tend to be computationally expensive for high dimensional problem solved over longer time-horizon. Even if nonlinear models are incorporated to approximate the dynamic models, MPC would result in determining a local optimal solution which may not be the best solution, i.e. the global optimal solution. Also, MPC is computationally expensive and therefore cannot be implemented in engine control units.
Other prior art techniques include for example engine mapping. This technique conducts a series of tests on the engine and the program which controls it, and implements steady-state engine response as control variables to determine the inputs to the engine, which establishes the operating limits of the engine and sets control input bias with respect to the operating point, known as a steady-state calibration. Then, these input settings are graphically represented in the form of a characteristic map, which shows the performance curves that represent performance of the engine when there is a change in certain parameters such as speed, load, air-fuel ratio, as well as engine/ambient temperature. Most of the calibration techniques utilized rely on a person to perform off-line calibration and optimization and subsequently plug in values in the engine control module (ECM) for engine operation. These techniques apply post-processing to data collected in a controlled environment for calibration and optimization. However, off-line calibration requires a lot of statistics and data to prepare the engine for actual use, during which the engine will likely encounter situations and states that are not covered by the initial static dataset used for off-line calibration. As such, because real operating conditions can be drastically different from the conditions during calibration, such techniques are not adequate in adapting the engine to real conditions as it operates. Similar maps are designed for transient states that are tuned via trial-and-error processes where the calibrator runs different duty cycles and calibrate to meet the expected performance. Because it is not possible to run all the duty cycles in practice, such processes may lead to suboptimal performance for some cycles. Furthermore, because the calibration techniques are performed to model the engine behavior only in steady state, during the transient state the engine is controlled to meet a specific objective such as smoke or torque response, and thus other variables such as fuel consumption are typically given less weight when considering such factors during engine operation.
Therefore, there is a need to provide a more computationally efficient, real-time engine tuning technique which allows for a more accurate prediction of the actual engine behavior in a dynamic style to enable optimization of the air handling and fueling system within the engine, all the while reducing the dependency on the accuracy of the initial prediction model and frequent calibrations.